PyTorch-线性回归

已经进入大模微调的时代，但是学习pytorch，对后续学习rasa框架有一定帮助吧。

x_train = np.array([[3.3], [4.4], [5.5], [6.71], [6.93], [4.168],
                    [9.779], [6.182], [7.59], [2.167], [7.042],
                    [10.791], [5.313], [7.997], [3.1]], dtype=np.float32)
y_train = np.array([[1.7], [2.76], [2.09], [3.19], [1.694], [1.573],
                    [3.366], [2.596], [2.53], [1.221], [2.827],
                    [3.465], [1.65], [2.904], [1.3]], dtype=np.float32)


x_train = torch.from_numpy(x_train)
y_train = torch.from_numpy(y_train)


class linearRegression(nn.Module):
    def __init__(self):
        super(linearRegression, self).__init__()
        self.linear = nn.Linear(1, 1)  # input and output is 1 dimension

    def forward(self, x):
        out = self.linear(x)
        return out
model = linearRegression()


criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=1e-4)


num_epochs = 1000
for epoch in range(num_epochs):
    inputs = Variable(x_train)
    target = Variable(y_train)
 
    # forward
    out = model(inputs) # 前向传播
    loss = criterion(out, target) # 计算loss
 
    # backward
    optimizer.zero_grad() # 梯度归零
    loss.backward() # 反向传播
    optimizer.step() # 更新参数
 
    if (epoch 1) % 20 == 0:
         print(f'Epoch[{epoch+1}/{num_epochs}], loss: {loss.item():.6f}')


model.eval()
predict = model(Variable(x_train))
predict = predict.data.numpy()


fig = plt.figure(figsize=(10, 5))
plt.plot(x_train.numpy(), y_train.numpy(), 'ro', label='Original data')
plt.plot(x_train.numpy(), predict, label='Fitting Line')

plt.legend() 
plt.show()


torch.save(model.state_dict(), './linear.pth')